Types of Segmentation
Transcript
Hello, I'm Beth Horn, SVP Advanced Analytics at Decision Analyst. In this episode, I will discuss types of consumer segmentation.
Historically, attitudinal and needs-based segmentations have been the standard. While valuable for understanding psychographics, values, and behaviors, they often lack the precision needed for effective targeting. Specifically, the ability to locate and message to particular segments. To address this, location-based segmentation evolved. It relies on the premise that people living in the same neighborhood share similar incomes, backgrounds, and lifestyles. By appending these location-based clusters to customer data, companies can craft highly targeted campaigns that appeal to specific geographic segments.
Transaction-based segmentation is another powerful method. It uses purchase behavior from loyalty programs, app usage, or online history to define segments. This creates a purely behavioral view of the market. Or it can augment survey data to show not just what people say they do but what they actually do.
Then there is demand landscape segmentation which overlays needs with usage occasions. It acknowledges that a consumer's needs change throughout the day. A person might need a boost of energy in the morning but relaxation at night. This helps companies understand exactly how their products fit into the ebb and flow of consumers lives.
Finally, there is micro segmentation. This technique identifies small niche groups of consumers who are highly similar. For example, a micro segment might be female outdoor enthusiasts aged 18 to 24 who follow a vegan diet. While small, perhaps 1% of the population, their spending power in that specific category is high, making them incredibly valuable to the right brand.
It's worth noting that often AI and machine learning are the engines powering many of these segmentation types, allowing us to find patterns in the data that humans just can't see.
Thank you for watching and be sure to check out our other segmentation episodes.
Presenter
Elizabeth Horn
Senior VP, Advanced Analytics
Beth has provided expertise and high-end analytics for Decision Analyst for over 25 years. She is responsible for design, analyses, and insights derived from discrete choice models; MaxDiff analysis; volumetric forecasting; predictive modeling; GIS analysis; and market segmentation. She regularly consults with clients regarding best practices in research methodology. Beth earned a Ph.D. and a Master of Science in Experimental Psychology with emphasis on psychological principles, research methods, and statistics from Texas Christian University in Fort Worth, TX.